Exploring Patients’ Lived Experiences of Trust and Privacy in AI-Driven Health Chatbots for Chronic Illness Care
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Abstract
Artificial intelligence (AI) has become integral to digital healthcare, with AI-based chatbots increasingly supporting chronic disease management. Despite proven functional advantages, little is known about how patients perceive trust, privacy, and emotional connection in these non-human interactions. Previous research has emphasized usability and efficiency, leaving unexplored the emotional and relational dimensions of AI-mediated care. This study examines how patients with chronic conditions experience trust and privacy when interacting with AI-driven health chatbots, employing an interpretative phenomenological approach. Semi-structured interviews with 12 long-term chatbot users were thematically analyzed. Four key themes emerged: ambivalent trust, emotional distance in algorithmic care, negotiated privacy, and the tension between being heard and understood. Findings highlight the paradox of digital care—patients value accessibility but seek relational depth often missing in AI interfaces. This study reveals that trust in AI health tools is fluid, context-dependent, and emotionally nuanced, offering insights for more human-centered and ethically transparent AI design.
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